We prove generalization error bounds of classes of low-rank matrices with some norm constraints for collaborative filtering tasks. Our bounds are tighter, compared to known bounds using rank or the related quantity only, by taking the additional L1 and L∞ constraints into account. Also, we show that our bounds on the Rademacher complexity of the classes are optimal.

Weissman introduced a coding problem for channels with action-dependent states. In this coding problem, there are two encoders and a decoder. An encoder outputs an action that affects the state of the channel. Then, the other encoder outputs a codeword of the message into the channel by using the channel state. The decoder receives a noisy observation of the codeword, and reconstructs the message. In this paper, we show an exponential error bound for channels with action-dependent states based on the random coding argument.

This letter investigates the training convergence in range-based cooperative positioning with stochastic positional knowledge. Firstly, a closed-form of squared position-error bound (SPEB) is derived with error-free ranging. Using the derived closed-form, it is proved that the SPEB reaches its minimum when at least 2 out of N (> 2) agents send training sequences. Finally, numerical results are provided to elaborate the theoretical analysis with zero-mean Gaussian ranging errors.

In and we have presented a simulation-based algorithm for optimizing the average reward in a parameterized continuous-time, finite-state semi-Markov Decision Process (SMDP). We approximated the gradient of the average reward. Then, a simulation-based algorithm was proposed to estimate the approximate gradient of the average reward (called GSMDP), using only a single sample path of the underlying Markov chain. GSMDP was proved to converge with probability 1. In this paper, we give bounds on the approximation and estimation errors for GSMDP algorithm. The approximation error of that approximation is the size of the difference between the true gradient and the approximate gradient. The estimation error, the size of the difference between the output of the algorithm and its asymptotic output, arises because the algorithm sees only a finite data sequence.

Harmonic balance (HB) method is well known principle for analyzing periodic oscillations on nonlinear networks and systems. Because the HB method has a truncation error, approximated solutions have been guaranteed by error bounds. However, its numerical computation is very time-consuming compared with solving the HB equation. This paper proposes an algebraic representation of the error bound using Grobner base. The algebraic representation enables to decrease the computational cost of the error bound considerably. Moreover, using singular points of the algebraic representation, we can obtain accurate break points of the error bound by collisions.

This letter considers the signal design problems for quaternary digital communications with nonuniform sources. The designs are considered for both the average and equal energy constraints and for a two-dimensional signal space. A tight upper bound on the bit error probability (BEP) is employed as the design criterion. The optimal quarternary signal sets are presented and their BEP performance is compared with that of the standard QPSK and the binary signal set previously designed for nonuniform sources. Results shows that a considerable saving in the transmitted power can be achieved by the proposed average-energy signal set for a highly nonuniform source.

The circular decoding algorithm for tail-biting convolutional codes is executed using a fixed number of computations and is suitable for DSP/ASIC implementations. This letter presents the performance and complexity trade-off in the circular decoding algorithm using an analytic bound on the error probability. An incremental performance improvement is shown as the complexity increases from O(L) to O(L+10K) where L is the length of the decoding trellis and K is the constraint length. The decoding complexity required to produce the maximum-likelihood performance is presented, which is applicable to many codes of practical interest.